| Features |
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übertool has an impressive list of features as shown below. This is the feature list of version
1.1
- Data Integration
- Biological Object Model
- Public and proprietary data
- Validation layer
- Data export to excel and other formats
- Import and integration from diverse sources:
- Expression data: Affymetrix cell files, data in 'Eisen' format, tab separated
- Sequence data: Genbank, FASTA formatted files, Swissprot format
- Enzyme Nomenclature files
- Gene Ontology
- NCBI Locus link
- NCBI Taxonomy
- OMIM
- PDB
- PFAM
- Prosite
- Unigene
- BRENDA
- KEGG
- and more
- Method Integration
- Methods from various disciplines for analysis of
- Sequence
- Structure
- Expression data
- Taxonomy and Phylogeny
- Metabolic pathways
- Workflows
- Automatic checking of data compatibility
- Background execution of workflows
- Available methods:
- Bioinformatics
- Blast and Psi-Blast
- Sequence alignment: Smith Waterman and Needleman-Wunsch
- Multiple alignment similar to ClustalW
- Hidden Markov Profiles (aka Pfam)
- Hidden Markov Models
- Sequence repeat detection
- ORF Detection and Gene finding
- Codon usages and Compositional analysis
- Sequence Vectorization
- Transcription and Translation
- Function analysis for cluster of sequences
- Creating and mathing of sequence motif
- Match sequence profiles and regular expressions
- Secondary structure determination
- Structure superposition
- Substructure extraction
- Expression matrix processing: Completion, Normalization, Filtering and Masking
- Mapping of expression data to metabolic pathways, Gene Ontology
- Metabolic pathway reconstruction
- Clustering, Machine learning
- K-means clustering
- Principal Component Analysis (PCA)
- Support Vector Machines (SVM)
- Self Organizing Maps (SOM)
- Various hierarchical cluster algorithms
- Various distance/similarity measures
- Distance trees, UPGMA
- Neighbor joining
- General
- Vector operations
- Sorting
- Shuffling
- Statistics (average, mean, etc.)
- Visual Orientation
- Graphical workflow representation
- Drag'n drop workflow construction
- Hierarchical tabular viewers
- Diverse graphical viewers for biological data and workflow results
- Advantages
- Shallow learning curve thanks to visual programming
- Unified data structure
- Expandability through script language (python)
- Scalability from desktop to corporation
- Company-wide sharing of results and methods
- Applications
- Whatever you can visualize:
- Sequence analysis
- Gene annotation
- Expression analysis
- Classification of data through machine learning approaches
- Target finding
and any combination thereof!